Learning with Group Noise
Authors: Qizhou Wang, Jiangchao Yao, Chen Gong, Tongliang Liu, Mingming Gong, Hongxia Yang, Bo Han10192-10200
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The performance on a range of real-world datasets in the area of several learning paradigms demonstrates the effectiveness of Max-Matching. We conduct a range of experiments, and the results indicate that the proposed method can achieve superior performance over baselines from three different learning paradigms with group noise in Figure 1. |
| Researcher Affiliation | Collaboration | Qizhou Wang1,2,*, Jiangchao Yao3,*, Chen Gong2,4, , Tongliang Liu5, Mingming Gong6, Hongxia Yang3, Bo Han 1, 1 Department of Computer Science, Hong Kong Baptist University 2 Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Mo E, School of Computer Science and Engineering, Nanjing University of Science and Technology 3 Data Analytics and Intelligence Lab, Alibaba Group 4 Department of Computing, Hong Kong Polytechnic University 5 Trustworthy Machine Learning Lab, School of Computer Science, Faculty of Engineering, The University of Sydney 6 School of Mathematics and Statistics, The University of Melbourne |
| Pseudocode | No | The paper describes the proposed Max-Matching method in detail and illustrates its structure in Figure 2, but it does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the Max-Matching method is publicly available. |
| Open Datasets | Yes | The experiments are conducted on an object localization dataset SIVAL (Rahmani et al. 2005) in the literature of MIL, as it provides instance-level annotations for evaluation. The experiments are conducted on five PLL datasets from various domains: FG-NET (Panis and Lanitis 2014) aims at facial age estimation; MSRCv2 (Liu and Dietterich 2012) and Bird Song (Briggs, Fern, and Raich 2012) focus on object classification; Yahoo! News (Guillaumin, Verbeek, and Schmid 2010) and Lost (Cour, Sapp, and Taskar 2011) deal with face naming tasks. The offline experiments are implemented on a range of datasets from Amazon: Video, Beauty, and Game. |
| Dataset Splits | Yes | Each dataset is then partitioned into 8:1:1 for training, validation, and test. Each dataset is partitioned randomly into 8:1:1 for training, validation, and test. For each user, we randomly take two subsets for validation and test, and the remaining data are used for training. |
| Hardware Specification | No | The paper describes the experimental settings and datasets used, but it does not provide specific details about the hardware used to run the experiments (e.g., GPU/CPU models, memory, or cloud instances). |
| Software Dependencies | No | The paper states 'Moreover, we implement Max-Matching using PyTorch, the Adam (Kingma and Ba 2015) is adopted with the learning rate selected from {10 1, , 10 4}, and the methods are run for 50 epochs.' However, it does not specify version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | Moreover, we implement Max-Matching using PyTorch, the Adam (Kingma and Ba 2015) is adopted with the learning rate selected from {10 1, , 10 4}, and the methods are run for 50 epochs. |